Expanding semantic classes via user feedback

ABSTRACT

The present technology extends to methods, systems, and computer program products for expanding semantic classes via user feedback. Aspects of the technology learn how a set of labels can be expanded from user-generated tags. Text labels applied by human reviewers to digital content can be inspected and compared to one another. When a threshold of human-generated text tags contain similar terminology, the set of labels can be expanded to define a representation of the similar terminology. Similar terminology can include terms that originate from the same base term, are synonyms, are more specific terms related to a general term category, etc. Similar terminology can be consolidated into a defining term that is used to generate a new (more granular) label or a new top level label. Accordingly, new semantic classes can be discovered from user-generated feedback. New semantic classes can provide a more granular representation of content item classification.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation and claims the benefit of U.S.application Ser. No. 16/536,043, filed on Aug. 8, 2019, which isexpressly incorporated by reference herein in its entirety.

BACKGROUND 1. Technical Field

This technology relates generally to expanding sets of semantic classesvia user feedback, and, more particularly, to detecting types of speechwithin content items.

2. Related Art

Social media networks conventionally attempt to prevent propagation oftoxic speech, such as, for example, racial slurs, aggressive language,homophobic language, etc. When toxic speech is detected in a message,social media networks can delete the message and take disciplinaryaction (e.g., suspension, ban, etc.) against the account that posted themessage. Social media networks can use human moderators as well as someautomated systems to identify toxic speech. Due to the sheer volume ofsocial media interactions, many social media networks also rely on theirusers to alert them to messages that possibly contain toxic speech. Forexample, users can tag messages that are then reviewed by humanmoderators.

BRIEF DESCRIPTION OF THE DRAWINGS

The specific features, aspects and advantages of the present technologywill become better understood with regard to the following descriptionand accompanying drawings where:

FIG. 1 illustrates an example block diagram of a computing device.

FIG. 2 illustrates an example computer architecture that facilitatesexpanding a semantic class set based on user-generated feedback.

FIG. 3 illustrates a flow chart of an example method for expanding asemantic class set based on user-generated feedback.

FIG. 4 illustrates another computer architecture that facilitatesexpanding a semantic class set based on user-generated feedback.

DETAILED DESCRIPTION

The present technology extends to methods, systems, and computer programproducts for expanding semantic classes via user feedback.

User feedback loops can start with a pre-defined, fixed set of labels,such as, for example, true/false, relevant/not-relevant,toxic/non-toxic, etc. For example, an initial set of labels for labelingcontent items (e.g., social media network interactions) can includevarious semantic classes, such as, for example, {toxic, non-toxic}.Aspects of the technology additionally learn how a set of labels can beexpanded from user-generated tags. A user (e.g., a human moderator in asocial media network) can tag a content item (e.g., a message) as aparticular type of content item (e.g., as “toxic”). The tagged contentitem (e.g., message) can be forwarded to human moderators and/or otherautomated systems. The human moderators and/or automated systems canconfirm (or deny) the content item type. For example, a human moderatorand/or automated system can confirm or deny that a content item (e.g.,an image, a video, audio, a web page, an email address, a forum post, amessage, etc.) contains toxic content, such as, language, terminology,images, sounds, etc.

Content contained in similarly tagged content items (e.g., “toxic”messages) can be inspected and compared to one another. When a threshold(e.g., an amount, a percentage, etc.) of similarly tagged content items(e.g., “toxic” messages) contain similar terminology, the set of labelscan be expanded to define a representation of the similar terminology.Within content items, similar terminology can include terms thatoriginate from the same base term, are synonyms, are more specific termsrelated to a general term category, etc.

In one example, it may be that a specified number of messages includingone or more of terms: {race, racism, racist, racial-prejudice,race-discrimination} are confirmed as “toxic”. The terms {race, racism,racist, racial-prejudice, race-discrimination} can beresolved/consolidated into a new sematic class of “toxic-racist”. Theinitial set of labels can then be refined/expanded to include a{toxic-racist} label. For example, the initial set of labels can berefined/expanded to: {toxic-racist, toxic-other, non-toxic}

When terminology in other “toxic” messages exceeds a threshold, theterminology can be resolved/consolidated into additional semanticclasses. For example, one or more terms can be resolved/consolidatedinto a new sematic class of “toxic-gun-violence”. The refined/expandedset of labels can then be further refined/expanded to include a{toxic-gun-violence} label. For example, the refined/expanded set oflabels can be further refined/expanded to: {toxic-racist,toxic-gun-violence, toxic-other, non-toxic}.

Multiple levels of refinement/expansion are possible and a hierarchy ofclasses can be generated. Continuing with the example, a threshold ofmessages confirmed as “toxic-gun-violence” may include terminologyrelated to mass shootings. The further refined/expanded set of labelscan then be additionally refined/expanded to include a{toxic-gun-violence-mass-shooting} label. For example, the furtherrefined/expanded set of labels can be additionally refined/expanded to:{toxic-racist, toxic-gun-violence-mass-shooting,toxic-gun-violence-other, toxic-other, non-toxic}.

Accordingly, new semantic classes can be discovered from user-generatedfeedback. The new semantic classes can provide a more granularrepresentation of content item (e.g., message) classification.

Aspects can be implemented at any system(s) that generates contentitems. In one aspect, a set of labels is used across multiple contentitem generating systems, such as, for example, across multiple socialmedia networks. As such, terminology identified by a content itemgenerating system (e.g., one social media network) can be propagated tocontent item classification at another content item generating system(e.g., message classification at another social media network).

FIG. 1 illustrates an example block diagram of a computing device 100.Computing device 100 can be used to perform various procedures, such asthose discussed herein. Computing device 100 can function as a server, aclient, or any other computing entity. Computing device 100 can performvarious communication and data transfer functions as described hereinand can execute one or more application programs, such as theapplication programs described herein. Computing device 100 can be anyof a wide variety of computing devices, such as a mobile telephone orother mobile device, a desktop computer, a notebook computer, a servercomputer, a handheld computer, tablet computer and the like.

Computing device 100 includes one or more processor(s) 102, one or morememory device(s) 104, one or more interface(s) 106, one or more massstorage device(s) 108, one or more Input/Output (I/O) device(s) 110, anda display device 130 all of which are coupled to a bus 112. Processor(s)102 include one or more processors or controllers that executeinstructions stored in memory device(s) 104 and/or mass storagedevice(s) 108. Processor(s) 102 may also include various types ofcomputer storage media, such as cache memory.

Memory device(s) 104 include various computer storage media, such asvolatile memory (e.g., random access memory (RAM) 114) and/ornonvolatile memory (e.g., read-only memory (ROM) 116). Memory device(s)104 may also include rewritable ROM, such as Flash memory.

Mass storage device(s) 108 include various computer storage media, suchas magnetic tapes, magnetic disks, optical disks, solid state memory(e.g., Flash memory), and so forth. As depicted in FIG. 1 , a particularmass storage device is a hard disk drive 124. Various drives may also beincluded in mass storage device(s) 108 to enable reading from and/orwriting to the various computer readable media. Mass storage device(s)108 include removable media 126 and/or non-removable media.

I/O device(s) 110 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 100.Example I/O device(s) 110 include cursor control devices, keyboards,keypads, barcode scanners, microphones, monitors or other displaydevices, speakers, printers, network interface cards, modems, cameras,lenses, radars, CCDs or other image capture devices, and the like.

Display device 130 includes any type of device capable of displayinginformation to one or more users of computing device 100. Examples ofdisplay device 130 include a monitor, display terminal, video projectiondevice, and the like.

Interface(s) 106 include various interfaces that allow computing device100 to interact with other systems, devices, or computing environmentsas well as humans. Example interface(s) 106 can include any number ofdifferent network interfaces 120, such as interfaces to personal areanetworks (PANs), local area networks (LANs), wide area networks (WANs),wireless networks (e.g., near field communication (NFC), Bluetooth,Wi-Fi, etc., networks), and the Internet. Other interfaces include userinterface 118 and peripheral device interface 122.

Bus 112 allows processor(s) 102, memory device(s) 104, interface(s) 106,mass storage device(s) 108, and I/O device(s) 110 to communicate withone another, as well as other devices or components coupled to bus 112.Bus 112 represents one or more of several types of bus structures, suchas a system bus, PCI bus, IEEE 1394 bus, USB bus, and so forth.

FIG. 2 illustrates an example computer architecture 200 that facilitatesexpanding a semantic class set based on user-generated feedback. Asdepicted, users 201 have accounts with and/or use social media networks202 (or other content item generating systems). Users 201 can interact(e.g., post messages and other content, view messages and other content,exchange messages and other content, etc.) with one another via socialmedia networks 202 (or the other content item generating systems). Eachof users 201 can have accounts with one or more social media networks(or other content item generating systems). For example, user 201A maybe have account with social media networks 202A and 202B, user 201B mayhave an account with social media networks 202A, 202B, and 202C, user201C may have an account with social media networks 202A and 202C, etc.

In one aspect, users 201 participate in social media interactions 203(or other content item interactions) with one another across one or moreof social media networks 202 (or other content item generating systems).Users 201 can classify social media interactions 203 (or other contentitem interactions) of other users 201 into any of classes 221A, 221B,etc. included in semantic class set 221. In another aspect, moderatorsmoderate content items, such as, messages exchanged between users. Themoderators can classify social media interactions 203 (or other contentitem interactions into any of classes 221A, 221B, etc. Classificationscan occur through free-form input during reporting of objectionablecontent

In one aspect, class 221A can be used to classify social mediainteractions (or other content item interactions) as “toxic”interactions and class 221B can be used to classify social mediainteractions (or other content item interactions) as “non-toxic”interactions. Social media networks 202 (or other content itemgenerating systems) can indicate user classification of social mediainteractions 203 in interaction classifications 204 (or of other contentitem interactions in other classifications). A user that classifies acontent item interaction (e.g., a message) can also annotate (tag) the(e.g., social media) interaction indicating why the interaction was soclassified. The annotation can be considered part of the content of theinteraction.

In general, semantic class computation module 211 can receive socialmedia interactions 203 and interaction classifications 204 from socialmedia networks 202 (or other content item interactions from othercontent item generating systems). Interaction classifier 212 canseparate social media interactions 203 (as well as other content iteminteractions) into different corpora by class based on interactionclassifications 204. Term consolidator 213 can inspect content withineach classified interaction corpus. Term consolidator 213 can determinewhen a threshold (e.g., quantity, percentage, etc.) of interactions in aclassified interaction corpus contain similar terminology. Similarterminology can include terms that originate from the same base term,are synonyms, are more specific terms (e.g., ammunition, bullets,shooting, etc.) related to a general term category (e.g., guns), etc.

When a threshold of interactions in a class contain similar terminology,term consolidator can consolidate the similarly terminology into arepresentative term. For example, a threshold number of social mediamessages (or other content items) may include one or more of the terms:{race, racism, racist, racial-prejudice, race-discrimination}. Termconsolidator 213 can consolidate the terms {race, racism, racist,racial-prejudice, race-discrimination} into the representative term“racist”. Term consolidator 213 can send the representative term and theclass to class generator 214.

Class generator 214 can receive a representative term and class fromterm consolidator 213. Class generator 214 can generate a new (moregranular) class by combining the representative term and the class intothe new class. Class expansion can include creating sub-classes from ahigher level class (vertical expansion) as well as creating new higher(e.g., top) level classes. For example, class generator 214 can generatea new class “toxic-homophobic”. Class generator 214 may also generate anadditional remainder class (e.g., “other”) for social media interactionsthat are within the class but that are not within the new (moregranular) class. For example, class generator 214 can generate a newclass “toxic-homophobic” and a new class “toxic-other”. “Toxic-other”can be used to label social media interactions as toxic when the socialmedia interactions are “Toxic” for a reason other than being“homophobic”

Class generator 214 can send newly generated class to set expander 216.Set expander 216 can expand a semantic class set by including newlygenerated classes in an existing semantic class set. Class generator 214can make semantic class sets available to users 201 and can permit users201 to classify social media interactions into semantic classes includedin semantic class sets.

A further example of vertical expansion incudes starting with atop-level semantic class, such as, terrorism. Sub-classes, such as,terrorism_recruitment, terrorism_inciting_violence, orterrorism_imminent_threat, can be created by examining the tags appliedby human reviewers (e.g., moderators).

An example of horizontal expansion incudes starting with three top-levelsemantic classes, such as, racism, sexism, and religious hate. A newfourth top-level semantic class physical_violence can be created byexamining the tags applied by human reviewers (e.g., moderators).

FIG. 3 illustrates a flow chart of an example method for expanding asemantic class set based on user-generated feedback. Method 300 will bedescribed with respect to the components and data in computerarchitecture 200.

Example method 300 is described relative to social media messages andsocial media interactions. However, method 300 can also be used on othercontent items and content item interactions generated and propagated viaother content item generating systems.

Semantic class computation module 211 can make semantic class set 221available to users 201. Semantic class computation module 211 can permitusers 201 to classify social media interactions occurring on socialmedia networks 202 into classes 221A, 221B, etc.

As described, social media interactions 203 can include users 201posting, reviewing, exchanging, etc. messages and other content (e.g.,audio, video, images, documents, annotations, web pages, emailaddresses, forum posts, etc.) via social media networks 202 (or othernetworks). Users 201 can also classify social media interactions ofother users into classes included in semantic class set 221 and annotatesocial media interactions indicating why they were so classified. Forexample, user 201A can classify a social media interaction associatedwith user 201B as being in class 221A, 221B, etc. In one aspect, class221A indicates that a social media interaction is “Toxic”. User 201A canalso annotate the message indicating why she or she classified thesocial media interaction as “Toxic”. Classified social media interactionclassifications can be included in interaction classifications 204.

Interaction classifier 212 can access social media interactions 203 andinteraction classifications 204 from social media networks 202. Based oninteraction classifications 204, interaction classifier 212 can separatesocial media interactions 203 into different corpora by class based oninteraction classifications 204. For example, interaction classifier 212can formulate classified interaction corpus 217, including interactions218 (a subset of social media interactions 203) classified in class 221A(e.g., “Toxic”).

Method 300 includes accessing a corpus of social media networkinteractions that social media network users have classified into anexisting semantic class, the existing semantic class included in a setof semantic classes (301). For example, term consolidator 213 can accessclassified interaction corpus 217, including interactions 218 classifiedin class 221A.

Method 300 includes detecting that a threshold of social mediainteractions included in the corpus of social media network interactionsinclude related terminology (302). For example, term consolidator 213can determine that a threshold of interactions 218 include relatedterminology 234. Method 300 includes consolidating the relatedterminology into a consolidated term (303). For example, termconsolidator 213 can consolidate related terminology 234 intoconsolidated term 223. Term consolidator 213 can send consolidated term223 and class 221A to class generator 214.

Method 300 includes generating a new semantic class including acombination of the existing semantic class and the consolidated term andthat defines a subset of terminology included in the existing semanticclass (304). For example, class generator 214 can generate class 222Acombining consolidated term 223 and class 221A. Class 222A can define asubset of terminology included in class 221A. For example, if class 221Ais “Toxic” and consolidated term 223 is “Racist”, class 222A may“Toxic-Racist”. Class generator 214 can send class 222A to set expander216.

In one aspect, class generator 214 may also generate class 222B. Class222B can be used to classify social media interactions that areassociated with class 221A but that are not associated with class 222A.For example, if class 222A is “Toxic-Racist”, class 222B may be“Toxic-Other”. Class generator 214 can send class 222B to set expander216. In another aspect, class 222B already exists from prior iterationsof class generation.

Method 300 includes expanding the set of semantic classes into anexpanded set of semantic classes including adding the new semantic classto the set of semantic classes (305). For example, set expander 216 canexpand semantic class set 221 into expanded semantic class set 222. Setexpander 216 can add class 222A (and optionally class 222B) to expandedsemantic class set 222. In one aspect, class 221A is split into classes222A and 222B and class 221A is removed from expanded semantic class set222.

Method 300 includes making the expanded set of semantic classesavailable to social media network users (306). For example, semanticclass computation module 211 can make expanded semantic class set 222available to users 201. Method 300 includes permitting social medianetwork users to classify social media interactions into the newsemantic class (307). For example, semantic class computation module 211can permit users 201 to classify social media interactions occurring onsocial media networks 202 into classes 222A, 222B, 221B, etc.

Users 201 can then classify social media interactions of other usersinto classes included in expanded semantic class set 222. For example,user 201B can classify a social media interaction associated with user201C as being in class 222A, 222B, 221B, etc. Further social mediainteractions and social media interaction classifications can be sent tosemantic class computation module 211. A further iteration of method 300can be performed to possibly generate additional new semantic classesand make those available to users 201. Thus, in one aspect, a hierarchyof classes is generated/refined over time as use of new terminologyexceeds thresholds.

FIG. 4 illustrates another computer architecture 400 that facilitatesexpanding a semantic class set based on user-generated feedback.Computer systems 403 can access initial semantic class set 401. Users402 can use computer systems 403 (e.g., connected to a social medianetwork) to tag social media interactions with classes included ininitial semantic class set 401 and create labeled comments 404. Labeledcomments 404 (e.g., annotations) can be analyzed to determineuser-applied tags 406. User-applied tags 406 can be stored in databaseof user-generated tags 407. Database of user-generated tags 407 can beresolved 408 into supplemental semantic class set 409. As depicted,supplemental semantic class set 409 includes initial semantic class set401 (S₀) plus {label_(z_1), . . . , label_(z_y)}. That is,S₁=S₀+{label_(z_1), . . . , label_(z_y)}. Users

In a more specific example, in a first iteration, a semantic class setincludes {toxic, non-toxic}. 10% of messages tagged “toxic” includeterminology (e.g., content, user annotations, etc.) in the set {race,racism, racist, racial-prejudice, race-discrimination}. 15% of messagestagged “toxic” include terminology (e.g., content, user annotations,etc.) in the set {gun, guns, gun-violence, weapon, shooting, bullets}.

In a second iteration, terms {race, racism, racist, racial-prejudice,race-discrimination} are resolved/consolidated to “racist” and terms{gun, guns, gun-violence, weapon, shooting, bullets} areresolved/consolidated to “gun-violence”. The semantic class set isexpanded from {toxic, non-toxic} to {toxic-racist, toxic-gun-violence,toxic-other, non-toxic}.

Aspects of the technology include using and can be implemented usingmachine learning, neural networks, and other automated mechanisms toreduce the workload of human moderators. For example, machine learningand neural network modules can be used to implement the functionality ofmodules included in semantic class computation module 211. Machinelearning and neural network modules can also expand semantic classesbased on user feedback more effectively and efficiently relative tohuman moderators.

In one aspect, one or more processors are configured to executeinstructions (e.g., computer-readable instructions, computer-executableinstructions, etc.) to perform any of a plurality of describedoperations. The one or more processors can access information fromsystem memory and/or store information in system memory. The one or moreprocessors can transform information between different formats, such as,for example, content items, content item classifications, social mediainteractions, social media interaction classifications, classifiedinteraction corpora, interactions, classes, related terminology,consolidated terms, and semantic class sets, etc.

System memory can be coupled to the one or more processors and can storeinstructions (e.g., computer-readable instructions, computer-executableinstructions, etc.) executed by the one or more processors. The systemmemory can also be configured to store any of a plurality of other typesof data generated by the described components, such as, for example,content items, content item classifications, social media interactions,social media interaction classifications, classified interactioncorpora, interactions, classes, related terminology, consolidated terms,and semantic class sets, etc.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed herein. Implementationswithin the scope of the present disclosure may also include physical andother computer-readable media for carrying or storingcomputer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links, which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash or other vehicle computer,personal computers, desktop computers, laptop computers, messageprocessors, hand-held devices, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile telephones, PDAs, tablets,pagers, routers, switches, various storage devices, and the like. Thedisclosure may also be practiced in distributed system environmentswhere local and remote computer systems, which are linked (either byhardwired data links, wireless data links, or by a combination ofhardwired and wireless data links) through a network, both performtasks. In a distributed system environment, program modules may belocated in both local and remote memory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the description and claims to refer to particular systemcomponents. As one skilled in the art will appreciate, components may bereferred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

It should be noted that the embodiments discussed above may comprisecomputer hardware, software, firmware, or any combination thereof toperform at least a portion of their functions. For example, a computersystem may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

At least some embodiments of the disclosure have been directed tocomputer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer useable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure to the precise form disclosed.Many modifications, variations, and combinations are possible in lightof the above teaching. Further, it should be noted that any or all ofthe aforementioned alternate implementations may be used in anycombination desired to form additional hybrid implementations of thedisclosure.

What is claimed:
 1. A computer-implemented method comprising: detecting that a threshold number of content items in a corpus of content items include related terminology, the corpus of content items being associated with initial semantic classes that classify the corpus of content items; based on the detection, generating, via a semantics class computer module, a consolidated term for a tag of a new semantic class based on the related terminology, the new semantic class being in addition to the initial semantic classes classifying the corpus of content items; and expanding the initial semantic classes into an expanded set of semantic classes by adding the tag corresponding to the new semantic class.
 2. The computer-implemented method of claim 1, further comprising: receiving a classification of a number of the content items into the new semantic class.
 3. The computer-implemented method of claim 1, wherein generating the new semantic class comprises generating a new semantic sub-class that defines a subset of terminology refining an existing semantic class.
 4. The computer-implemented method of claim 3, wherein the expanding the initial semantic classes further comprises: resolving a database including the tags into a supplemental semantic class comprising the initial semantic classes coupled with a set of labels including the consolidated term as a new label, wherein the new label is associated with the new semantic class.
 5. The computer-implemented method of claim 4, wherein the resolving the database expands the initial semantic classes by adding the new label that serves as a subset classification of the initial semantic classes.
 6. The computer-implemented method of claim 4, further comprising: in addition to the new label, adding a catch-all label to the set of labels associated with other tags that do not contain the related terminology.
 7. The computer-implemented method of claim 4, wherein the new semantic class classifies a different subset from other subsets of the initial semantic classes.
 8. The computer-implemented method of claim 4, further comprising: detecting that another threshold number of second content items of the corpus of content items include a second set of tags containing another set of related terminology; generating a second consolidated term based on the second set of tags; resolving the database including the second set of tags into a revised supplemental semantic class comprising the initial semantic classes coupled with a revised set of labels including the second consolidated term as a second new label; and receive a classification of a number of the content items to be classified into the revised supplemental semantic class.
 9. A system comprising: storage configured to store instructions; and one or more processors configured to execute the instructions and cause the one or more processors to: detect that a threshold number of content items in a corpus of content items include related terminology, the corpus of content items being associated with initial semantic classes that classify the corpus of content items; generate, via a semantics class computer module, a consolidated term for a tag of a new semantic class based on the related terminology, the new semantic class being in addition to the initial semantic classes classifying the corpus of content items; and expand the initial semantic classes into an expanded set of semantic classes by adding the tag corresponding to the new semantic class.
 10. The system of claim 9, wherein the one or more processors is configured to execute the instructions and cause the one or more processors to: receive a classification of a number of the content items into the new semantic class.
 11. The system of claim 9, wherein generating the new semantic class comprises generating a new semantic sub-class that defines a subset of terminology refining an existing semantic class.
 12. The system of claim 11, wherein the one or more processors is configured to execute the instructions and cause the one or more processors to: resolve a database including the tags into a supplemental semantic class comprising the initial semantic classes coupled with a set of labels including the consolidated term as a new label, wherein the new label is associated with the new semantic class.
 13. The system of claim 12, wherein the resolving the database expands the initial semantic classes by adding the new label that serves as a subset classification of the initial semantic classes.
 14. The system of claim 12, wherein the one or more processors is configured to execute the instructions and cause the one or more processors to: in addition to the new label, add a catch-all label to the set of labels associated with other tags that do not contain the related terminology.
 15. The system of claim 12, wherein the new semantic class classifies a different subset from other subsets of the initial semantic classes.
 16. The system of claim 12, wherein the one or more processors is configured to execute the instructions and cause the one or more processors to: detect that another threshold number of second content items of the corpus of content items include a second set of tags containing another set of related terminology; generate a second consolidated term based on the second set of tags; resolve the database including the second set of tags into a revised supplemental semantic class comprising the initial semantic classes coupled with a revised set of labels including the second consolidated term as a second new label; and receive a classification of a number of the content items to be classified into the revised supplemental semantic class.
 17. A non-transitory computer-readable medium comprising instructions, the instructions, when executed by a computing system, cause the computing system to: detect that a threshold number of content items in a corpus of content items include related terminology, the corpus of content items being associated with initial semantic classes that classify the corpus of content items; generate, via a semantics class computer module, a consolidated term for a tag of a new semantic class based on the related terminology, the new semantic class being in addition to the initial semantic classes classifying the corpus of content items; and expanding the initial semantic classes into an expanded set of semantic classes by adding the tag corresponding to the new semantic class.
 18. The non-transitory computer-readable medium of claim 17, wherein the non-transitory computer-readable medium further comprises instructions that, when executed by the computing system, cause the computing system to: receive a classification of a number of the content items into the new semantic class.
 19. The non-transitory computer-readable medium of claim 17, generating the new semantic class comprises generating a new semantic sub-class that defines a subset of terminology refining an existing semantic class.
 20. The non-transitory computer-readable medium of claim 19, wherein the non-transitory computer-readable medium further comprises instructions that, when executed by the computing system, cause the computing system to: resolve a database including the tags into a supplemental semantic class comprising the initial semantic classes coupled with a set of labels including the consolidated term as a new label, wherein the new label is associated with the new semantic class. 